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Open AccessEditor’s ChoiceArticle

Machine Learning-Based App for Self-Evaluation of Teacher-Specific Instructional Style and Tools

1
School of Physical and Mathematical Sciences, Nanyang Technological University, SPMS-MAS-05-23, 21 Nanyang Link, Singapore 637371, Singapore
2
School of Physical and Mathematical Sciences, Nanyang Technological University, SPMS-MAS-04-10, 21 Nanyang Link, Singapore 637371, Singapore
*
Author to whom correspondence should be addressed.
Educ. Sci. 2018, 8(1), 7; https://doi.org/10.3390/educsci8010007
Received: 27 September 2017 / Revised: 31 December 2017 / Accepted: 3 January 2018 / Published: 10 January 2018
(This article belongs to the Special Issue Collaborative Learning with Technology—Frontiers and Evidence)
Course instructors need to assess the efficacy of their teaching methods, but experiments in education are seldom politically, administratively, or ethically feasible. Quasi-experimental tools, on the other hand, are often problematic, as they are typically too complicated to be of widespread use to educators and may suffer from selection bias occurring due to confounding variables such as students’ prior knowledge. We developed a machine learning algorithm that accounts for students’ prior knowledge. Our algorithm is based on symbolic regression that uses non-experimental data on previous scores collected by the university as input. It can predict 60–70 percent of variation in students’ exam scores. Applying our algorithm to evaluate the impact of teaching methods in an ordinary differential equations class, we found that clickers were a more effective teaching strategy as compared to traditional handwritten homework; however, online homework with immediate feedback was found to be even more effective than clickers. The novelty of our findings is in the method (machine learning-based analysis of non-experimental data) and in the fact that we compare the effectiveness of clickers and handwritten homework in teaching undergraduate mathematics. Evaluating the methods used in a calculus class, we found that active team work seemed to be more beneficial for students than individual work. Our algorithm has been integrated into an app that we are sharing with the educational community, so it can be used by practitioners without advanced methodological training. View Full-Text
Keywords: learning analytics; predictive modelling; machine learning; symbolic regression; quasi-experiment; clickers; team-based learning; handwritten homework; online homework learning analytics; predictive modelling; machine learning; symbolic regression; quasi-experiment; clickers; team-based learning; handwritten homework; online homework
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MDPI and ACS Style

Duzhin, F.; Gustafsson, A. Machine Learning-Based App for Self-Evaluation of Teacher-Specific Instructional Style and Tools. Educ. Sci. 2018, 8, 7. https://doi.org/10.3390/educsci8010007

AMA Style

Duzhin F, Gustafsson A. Machine Learning-Based App for Self-Evaluation of Teacher-Specific Instructional Style and Tools. Education Sciences. 2018; 8(1):7. https://doi.org/10.3390/educsci8010007

Chicago/Turabian Style

Duzhin, Fedor; Gustafsson, Anders. 2018. "Machine Learning-Based App for Self-Evaluation of Teacher-Specific Instructional Style and Tools" Educ. Sci. 8, no. 1: 7. https://doi.org/10.3390/educsci8010007

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